Grounding Generative AI in Your Enterprise Knowledge Graph
Generic LLM applications fail in corporate environments due to hallucinations, lack of private context, and outdated training data. Tech AKM designs custom Retrieval-Augmented Generation (RAG) pipelines that ingest millions of internal documents and deliver factual, cited answers in milliseconds.
Advanced Retrieval Mechanics
- Hybrid Search (Dense + Sparse): Combining pgvector semantic cosine similarity with BM25 keyword matching for optimal recall across technical terminologies.
- Context-Aware Semantic Chunking: Intelligent document parsing that respects document hierarchy, tables, and code snippets rather than arbitrary token boundaries.
- Cross-Encoder Re-Ranking: Utilizing Cohere or BGE re-rankers to evaluate the top 50 retrieved candidates and present only the most relevant context to the model.
- Strict Citation Enforcement: Forcing LLMs to cite source chunk IDs and verifying assertions against the retrieved text prior to client transmission.
Enterprise Privacy & Isolation
Data never leaves your secure VPC. We deploy private embedding models and self-hosted vector databases compliant with GDPR, SOC 2, and internal governance frameworks.
Targeted SEO & Keyword Index
Trending primary topic & 10 high-intent secondary keywords
Primary Trending Pillar
Production RAG Architecture
High Commercial Intent
10 Targeted Secondary Keywords:
Retrieval-Augmented Generation Engine
pgvector Semantic Retrieval Systems
Milvus Vector Database Scaling
Hybrid BM25 Vector Search
LLM Hallucination Mitigation Strategy
Chunking Strategy Optimization
Enterprise Document Intelligence
Private LLM Embeddings Pipeline
Knowledge Graph RAG Integration
Sub-100ms Vector Search Latency